import sys
import numpy as np

import time
import sys
import warnings
import datetime
import gc
import pandas as pd
import matplotlib.pyplot as plt
import sklearn 
import os
import seaborn as sns
from pathlib import Path
import pickle
import plotly as pl
import feature_selection
from sklearn.model_selection import GridSearchCV
import data_prep 
import data_summary
import lime_ModelExplainer
import data_split
#import plot_residual
import reading_data

import eda
from lime_ModelExplainer import lime_explainer
%matplotlib inline  
sys.path.append('/datascience/home/ssaha/Test/')
sys.path.append('/datascience/home/ssaha/')
from sklearn.utils import resample
import imblearn


import mlModels
import pickle


import model_evaluation
from config_param import project_identifier
from directoryPath import mlresult_dir, parent_dir, parent_dir_project, mlresult_dir,  data_dir, input_dir, mlobjects
import directoryPath

from config_param import project_identifier

read Data¶

start= time.time()
df_2016= reading_data.read_data(path="/datascience/home/ssaha/input/c360_customeradt_in_market_lexus_082016/")
print(time.time() -start)

126.65054106712341

gc.collect()

df_sampled = resample(df_2016, replace=False,     # sample without replacement
                                 n_samples=1000,    
                                 random_state=123)

df_sampled.head()

df_sampled=df_sampled.reset_index(drop=True)

del df_2016

gc.collect()

0

start= time.time()
X= data_prep.find_indep_feat(df_sampled)
y = df_sampled["dep_purchase_lexus_new_sedan_6mo"] 
print(time.time() -start)

0.008263826370239258

Data Summary¶

data_summary.summary_function(X)

data_summary.get_missing_value_count(X)

 Time taken to execute the function is 0.019936323165893555

data_summary.get_zero_count_in_numeric_cols(X)

data_summary.get_one_count_in_numeric_cols(X)

data_summary.get_most_frequent_count(X)

EDA¶

cat_col= X.select_dtypes(include='O')
num_col=X.select_dtypes(exclude='O')

eda.plot_count_on_all_cat(df_sampled,y.name, cat_col)

eda.plot_dist_on_all_num(df_sampled,y.name, num_col)

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:488: RuntimeWarning:

invalid value encountered in true_divide

/root/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning:

invalid value encountered in double_scalars

/root/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:26: RuntimeWarning:

invalid value encountered in reduce

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:838: RuntimeWarning:

invalid value encountered in true_divide

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:135: RuntimeWarning:

Degrees of freedom <= 0 for slice

/root/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py:127: RuntimeWarning:

invalid value encountered in double_scalars

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

eda.plot_summary(X)

eda.plot_missing(X)

 Time taken to execute the function is 0.019841909408569336

eda.plot_unique(X)

sns.heatmap(X.corr())

<matplotlib.axes._subplots.AxesSubplot at 0x7fdcff5e0b70>

eda.plot_histogram(X['avg_assoc_length'].fillna(0))

/root/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning:

The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.

 mu = 1583.02 and sigma = 1783.34

eda.plot_bargraph(X['poc_16_18'])

eda.plot_scatter(X['avg_assoc_length'], X['max_assoc_length'], 'avg_assoc_length vs max_assoc_length', 'avg_assoc_length', 'max_assoc_length')

Data Prep¶

X_imputed= data_prep.missing_value_imputation(X, 'median', '0', 60)

Number of categorical features: 57
Number of numerical features: 373

 Time taken to execute the function is 0.735877275466919

X_imputed.select_dtypes(exclude='O').iloc[:,1:10].fillna(0).plot(kind='box', rot=45)

df= X_imputed.select_dtypes(exclude='O').copy()

outlier_treatment_dataframe(X_imputed).select_dtypes(exclude='O').iloc[:,1:10].fillna(0).plot(kind='box', rot=45)

X_out= outlier_treatment_dataframe(X_imputed)

/root/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:7772: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

/root/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:7772: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

X_stand= data_prep.standarization(X_out)

Number of categorical features: 57
Number of numerical features: 373

 Time taken to execute the function is 0.1862351894378662

/root/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/datascience/home/ssaha/RMModel/Python/data_prep.py:671: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

X_casted= data_prep.type_casting(X, 7)

Converting to categorical variable from numerical: owned_lexus_sedan_ind
Converting to categorical variable from numerical: owned_lexus_LUV_ind
Converting to categorical variable from numerical: owned_lexus_new_luv_ind
Converting to categorical variable from numerical: owned_lexus_cpo_luv_ind
Converting to categorical variable from numerical: owned_lexus_new_sedan_ind
Converting to categorical variable from numerical: owned_lexus_cpo_sedan_ind
Converting to categorical variable from numerical: owned_toyota_ind
Converting to categorical variable from numerical: owned_toyota_scion_ind
Converting to categorical variable from numerical: owned_lexus_ind
Converting to categorical variable from numerical: owned_scion_ind
Converting to categorical variable from numerical: owned_camry_ind
Converting to categorical variable from numerical: owned_corolla_ind
Converting to categorical variable from numerical: owned_rav4_ind
Converting to categorical variable from numerical: owned_prius_ind
Converting to categorical variable from numerical: owned_truck_ind
Converting to categorical variable from numerical: owned_suv_ind
Converting to categorical variable from numerical: owned_hybrid_ind
Converting to categorical variable from numerical: owned_big_engine_vehicle_ind
Converting to categorical variable from numerical: owned_4wd_vehicle_ind
Converting to categorical variable from numerical: multiple_vehicle_type_ind
Converting to categorical variable from numerical: owned_camry_no
Converting to categorical variable from numerical: owned_prius_no
Converting to categorical variable from numerical: owned_hybrid_no
Converting to categorical variable from numerical: ever_brand_migration_ind
Converting to categorical variable from numerical: owned_tundra_ind
Converting to categorical variable from numerical: owned_tacoma_ind
Converting to categorical variable from numerical: owned_is250_ind
Converting to categorical variable from numerical: owned_is350_ind
Converting to categorical variable from numerical: owned_is_ind
Converting to categorical variable from numerical: owned_es_ind
Converting to categorical variable from numerical: owned_rx_ind
Converting to categorical variable from numerical: owned_ls_ind
Converting to categorical variable from numerical: owned_gs_ind
Converting to categorical variable from numerical: owned_chr_ind
Converting to categorical variable from numerical: owned_chr_scion_ind
Converting to categorical variable from numerical: owned_highlander_ind
Converting to categorical variable from numerical: owned_sienna_ind
Converting to categorical variable from numerical: owned_4runner_ind
Converting to categorical variable from numerical: owned_avalon_ind
Converting to categorical variable from numerical: owned_hs250h_ind
Converting to categorical variable from numerical: owned_ct200h_ind
Converting to categorical variable from numerical: owned_white_vehicle_ind
Converting to categorical variable from numerical: owned_black_vehicle_ind
Converting to categorical variable from numerical: owned_silver_gray_vehicle_ind
Converting to categorical variable from numerical: owned_gold_brown_orange_vehicle_ind
Converting to categorical variable from numerical: owned_yellow_vehicle_ind
Converting to categorical variable from numerical: owned_green_vehicle_ind
Converting to categorical variable from numerical: owned_aqua_vehicle_ind
Converting to categorical variable from numerical: owned_blue_vehicle_ind
Converting to categorical variable from numerical: owned_purple_vehicle_ind
Converting to categorical variable from numerical: owned_brown_vehicle_ind
Converting to categorical variable from numerical: owned_gold_vehicle_ind
Converting to categorical variable from numerical: owned_red_vehicle_ind
Converting to categorical variable from numerical: owned_orange_vehicle_ind
Converting to categorical variable from numerical: owned_other_color_vehicle_ind
Converting to categorical variable from numerical: owned_gray_vehicle_ind
Converting to categorical variable from numerical: owned_vehicle_my_1970_80_ind
Converting to categorical variable from numerical: owned_vehicle_my_1981_90_ind
Converting to categorical variable from numerical: owned_vehicle_my_1991_00_ind
Converting to categorical variable from numerical: owned_vehicle_my_2001_10_ind
Converting to categorical variable from numerical: owned_vehicle_my_2011_20_ind
Converting to categorical variable from numerical: purchased_vehicle_last_2years_ind
Converting to categorical variable from numerical: purchased_lexus_sedan_last_2years_ind
Converting to categorical variable from numerical: purchased_lexus_sedan_last_3years_ind
Converting to categorical variable from numerical: purchased_lexus_LUV_last_2years_ind
Converting to categorical variable from numerical: purchased_lexus_LUV_last_3years_ind
Converting to categorical variable from numerical: purchased_camry_last_2years_ind
Converting to categorical variable from numerical: purchased_camry_last_3years_ind
Converting to categorical variable from numerical: purchased_scion_last_2years_ind
Converting to categorical variable from numerical: purchased_scion_last_3years_ind
Converting to categorical variable from numerical: purchased_toyota_scion_last_3years_ind
Converting to categorical variable from numerical: purchased_lexus_last_3years_ind
Converting to categorical variable from numerical: purchased_rav4_last_3years_ind
Converting to categorical variable from numerical: purchased_is_last_3years_ind
Converting to categorical variable from numerical: current_owned_lexus_sedan_ind
Converting to categorical variable from numerical: current_owned_lexus_LUV_ind
Converting to categorical variable from numerical: current_owned_lexus_new_luv_ind
Converting to categorical variable from numerical: current_owned_lexus_new_sedan_ind
Converting to categorical variable from numerical: current_owned_lexus_cpo_luv_ind
Converting to categorical variable from numerical: current_owned_lexus_cpo_sedan_ind
Converting to categorical variable from numerical: current_owned_toyota_ind
Converting to categorical variable from numerical: current_owned_toyota_scion_ind
Converting to categorical variable from numerical: current_owned_lexus_ind
Converting to categorical variable from numerical: current_owned_scion_ind
Converting to categorical variable from numerical: current_owned_camry_ind
Converting to categorical variable from numerical: current_owned_corolla_ind
Converting to categorical variable from numerical: current_owned_rav4_ind
Converting to categorical variable from numerical: current_owned_prius_ind
Converting to categorical variable from numerical: current_owned_tundra_ind
Converting to categorical variable from numerical: current_owned_tacoma_ind
Converting to categorical variable from numerical: current_owned_is_ind
Converting to categorical variable from numerical: current_owned_es_ind
Converting to categorical variable from numerical: current_owned_rx_ind
Converting to categorical variable from numerical: current_owned_ls_ind
Converting to categorical variable from numerical: current_owned_gs_ind
Converting to categorical variable from numerical: current_owned_chr_ind
Converting to categorical variable from numerical: current_owned_chr_scion_ind
Converting to categorical variable from numerical: current_owned_highlander_ind
Converting to categorical variable from numerical: current_owned_sienna_ind
Converting to categorical variable from numerical: current_owned_4runner_ind
Converting to categorical variable from numerical: current_owned_avalon_ind
Converting to categorical variable from numerical: past_owned_lexus_sedan_ind
Converting to categorical variable from numerical: past_owned_lexus_LUV_ind
Converting to categorical variable from numerical: past_owned_lexus_new_luv_ind
Converting to categorical variable from numerical: past_owned_lexus_new_sedan_ind
Converting to categorical variable from numerical: past_owned_lexus_cpo_luv_ind
Converting to categorical variable from numerical: past_owned_lexus_cpo_sedan_ind
Converting to categorical variable from numerical: past_owned_toyota_ind
Converting to categorical variable from numerical: past_owned_toyota_scion_ind
Converting to categorical variable from numerical: past_owned_lexus_ind
Converting to categorical variable from numerical: past_owned_scion_ind
Converting to categorical variable from numerical: past_owned_camry_ind
Converting to categorical variable from numerical: past_owned_corolla_ind
Converting to categorical variable from numerical: past_owned_rav4_ind
Converting to categorical variable from numerical: past_owned_prius_ind
Converting to categorical variable from numerical: past_owned_tundra_ind
Converting to categorical variable from numerical: past_owned_tacoma_ind
Converting to categorical variable from numerical: past_owned_is_ind
Converting to categorical variable from numerical: past_owned_es_ind
Converting to categorical variable from numerical: past_owned_rx_ind
Converting to categorical variable from numerical: past_owned_ls_ind
Converting to categorical variable from numerical: past_owned_gs_ind
Converting to categorical variable from numerical: past_owned_chr_ind
Converting to categorical variable from numerical: past_owned_chr_scion_ind
Converting to categorical variable from numerical: past_owned_highlander_ind
Converting to categorical variable from numerical: past_owned_sienna_ind
Converting to categorical variable from numerical: past_owned_4runner_ind
Converting to categorical variable from numerical: past_owned_avalon_ind
Converting to categorical variable from numerical: purchased_vehicle_new_ind
Converting to categorical variable from numerical: purchased_vehicle_used_ind
Converting to categorical variable from numerical: purchased_vehicle_cpo_ind
Converting to categorical variable from numerical: purchased_vehicle_toyota_ind
Converting to categorical variable from numerical: purchased_vehicle_lexus_ind
Converting to categorical variable from numerical: purchased_vehicle_scion_ind
Converting to categorical variable from numerical: purchased_vehicle_truck_ind
Converting to categorical variable from numerical: purchased_vehicle_suv_ind
Converting to categorical variable from numerical: purchased_vehicle_hybrid_ind
Converting to categorical variable from numerical: purchased_vehicle_big_engine_ind
Converting to categorical variable from numerical: purchased_vehicle_4wd_ind
Converting to categorical variable from numerical: purchased_vehicle_lexus_sedan_ind
Converting to categorical variable from numerical: purchased_vehicle_lexus_LUV_ind
Converting to categorical variable from numerical: purchased_vehicle_camry_ind
Converting to categorical variable from numerical: purchased_vehicle_corolla_ind
Converting to categorical variable from numerical: purchased_vehicle_rav4_ind
Converting to categorical variable from numerical: purchased_vehicle_prius_ind
Converting to categorical variable from numerical: first_purchase_ind
Converting to categorical variable from numerical: first_purchase_lexus_sedan_ind
Converting to categorical variable from numerical: first_purchase_lexus_LUV_ind
Converting to categorical variable from numerical: first_purchase_toyota_ind
Converting to categorical variable from numerical: first_purchase_scion_ind
Converting to categorical variable from numerical: first_purchase_lexus_ind
Converting to categorical variable from numerical: first_purchase_camry_ind
Converting to categorical variable from numerical: first_purchase_hybrid_ind
Converting to categorical variable from numerical: first_purchase_prius_ind
Converting to categorical variable from numerical: first_purchase_big_engine_ind
Converting to categorical variable from numerical: first_purchase_suv_ind
Converting to categorical variable from numerical: no_leased_vehicle
Converting to categorical variable from numerical: dofu_year1_ind
Converting to categorical variable from numerical: dofu_year2_ind
Converting to categorical variable from numerical: dofu_year3_ind
Converting to categorical variable from numerical: dofu_year4_ind
Converting to categorical variable from numerical: dofu_year5_ind
Converting to categorical variable from numerical: dofu_year5above_ind
Converting to categorical variable from numerical: toyota_care_status
Converting to categorical variable from numerical: tc_completed_ind
Converting to categorical variable from numerical: tc_active_ind
Converting to categorical variable from numerical: tc_only1_ind
Converting to categorical variable from numerical: tc_only2_ind
Converting to categorical variable from numerical: tc_only3_ind
Converting to categorical variable from numerical: tc_only4_ind
Converting to categorical variable from numerical: tc_only5_ind
Converting to categorical variable from numerical: first_purchase_luxury_vehicle_ind
Converting to categorical variable from numerical: owned_corolla_no
Converting to categorical variable from numerical: owned_toyota_sedan_ind
Converting to categorical variable from numerical: owned_toyota_family_ind
Converting to categorical variable from numerical: owned_toyota_sport_ind
Converting to categorical variable from numerical: owned_toyota_compact_ind
Converting to categorical variable from numerical: owned_toyota_compactsuv_ind
Converting to categorical variable from numerical: owned_toyota_fullsizesuv_ind
Converting to categorical variable from numerical: owned_toyota_hybrid_ind
Converting to categorical variable from numerical: owned_toyota_luxurymidsize_ind
Converting to categorical variable from numerical: owned_toyota_midsize_ind
Converting to categorical variable from numerical: owned_toyota_midsizesuv_ind
Converting to categorical variable from numerical: owned_toyota_pickup_ind
Converting to categorical variable from numerical: owned_toyota_premiummidsize_ind
Converting to categorical variable from numerical: owned_toyota_sportscar_ind
Converting to categorical variable from numerical: owned_toyota_subcompact_ind
Converting to categorical variable from numerical: owned_toyota_subcompactsuv_ind
Converting to categorical variable from numerical: owned_toyota_van_ind
Converting to categorical variable from numerical: past_owned_toyota_compact_ind
Converting to categorical variable from numerical: past_owned_toyota_compactsuv_ind
Converting to categorical variable from numerical: past_owned_toyota_fullsizesuv_ind
Converting to categorical variable from numerical: past_owned_toyota_hybrid_ind
Converting to categorical variable from numerical: past_owned_toyota_luxurymidsize_ind
Converting to categorical variable from numerical: past_owned_toyota_midsize_ind
Converting to categorical variable from numerical: past_owned_toyota_midsizesuv_ind
Converting to categorical variable from numerical: past_owned_toyota_pickup_ind
Converting to categorical variable from numerical: past_owned_toyota_premiummidsize_ind
Converting to categorical variable from numerical: past_owned_toyota_sportscar_ind
Converting to categorical variable from numerical: past_owned_toyota_subcompact_ind
Converting to categorical variable from numerical: past_owned_toyota_subcompactsuv_ind
Converting to categorical variable from numerical: past_owned_toyota_van_ind
Converting to categorical variable from numerical: current_owned_toyota_compact_ind
Converting to categorical variable from numerical: current_owned_toyota_compactsuv_ind
Converting to categorical variable from numerical: current_owned_toyota_fullsizesuv_ind
Converting to categorical variable from numerical: current_owned_toyota_hybrid_ind
Converting to categorical variable from numerical: current_owned_toyota_luxurymidsize_ind
Converting to categorical variable from numerical: current_owned_toyota_midsize_ind
Converting to categorical variable from numerical: current_owned_toyota_midsizesuv_ind
Converting to categorical variable from numerical: current_owned_toyota_pickup_ind
Converting to categorical variable from numerical: current_owned_toyota_premiummidsize_ind
Converting to categorical variable from numerical: current_owned_toyota_sportscar_ind
Converting to categorical variable from numerical: current_owned_toyota_subcompact_ind
Converting to categorical variable from numerical: current_owned_toyota_subcompactsuv_ind
Converting to categorical variable from numerical: current_owned_toyota_van_ind
Converting to categorical variable from numerical: past_owned_toyota_4wd_ind
Converting to categorical variable from numerical: past_owned_toyota_family_ind
Converting to categorical variable from numerical: past_owned_toyota_sedan_ind
Converting to categorical variable from numerical: past_owned_toyota_sport_ind
Converting to categorical variable from numerical: past_owned_toyota_suv_ind
Converting to categorical variable from numerical: past_owned_toyota_truck_ind
Converting to categorical variable from numerical: first_purchase_sport_vehicle_ind
Converting to categorical variable from numerical: driver_ind
Converting to categorical variable from numerical: owner_ind
Converting to categorical variable from numerical: sales_survey_ind
Converting to categorical variable from numerical: service_survey_ind
Converting to categorical variable from numerical: sales_would_return_ind
Converting to categorical variable from numerical: sales_would_recommend_ind
Converting to categorical variable from numerical: service_would_return_ind
Converting to categorical variable from numerical: service_would_recommend_ind
Converting to categorical variable from numerical: has_uss
Converting to categorical variable from numerical: multiple_case_type_ind
Converting to categorical variable from numerical: has_cec
Converting to categorical variable from numerical: lhr_incomplete_lead_ind
Converting to categorical variable from numerical: lhr_sweepstakes_1_permission_ind
Converting to categorical variable from numerical: current_lead
Converting to categorical variable from numerical: current_handraiser
Converting to categorical variable from numerical: current_participator
Converting to categorical variable from numerical: ever_lead_ind
Converting to categorical variable from numerical: ever_handraiser_ind
Converting to categorical variable from numerical: ever_participator_ind
Converting to categorical variable from numerical: has_emd
Converting to categorical variable from numerical: communication_received_ind
Converting to categorical variable from numerical: campaign_received_ind
Converting to categorical variable from numerical: offer_received_ind
Converting to categorical variable from numerical: communication_opened_ind
Converting to categorical variable from numerical: communication_clicked_ind
Converting to categorical variable from numerical: campaign_opened_ind
Converting to categorical variable from numerical: campaign_clicked_ind
Converting to categorical variable from numerical: brochure_requested_ind
Converting to categorical variable from numerical: active_offer_ind
Converting to categorical variable from numerical: dealer_loyalist
Converting to categorical variable from numerical: noros_high_cust_pay_18_24mo
Converting to categorical variable from numerical: noros_high_cust_pay_12_18mo
Converting to categorical variable from numerical: noros_high_cust_pay_6_12mo
Converting to categorical variable from numerical: noros_high_cust_pay_0_6mo
Converting to categorical variable from numerical: noros_high_ro_time_12_18mo
Converting to categorical variable from numerical: noros_high_ro_time_0_6mo
Converting to categorical variable from numerical: no_ro_visit_during_tc
Converting to categorical variable from numerical: no_ro_visits_post_0_2_tc
Converting to categorical variable from numerical: no_ro_visits_post_2_4_tc
Converting to categorical variable from numerical: no_ro_visits_post_4_6_tc
Converting to categorical variable from numerical: no_ro_visits_post_over6_tc
Converting to categorical variable from numerical: ro_after_ocpe_ind
Converting to categorical variable from numerical: ever_customer_payment_ind
Converting to categorical variable from numerical: ever_warranty_payment_ind
Converting to categorical variable from numerical: ever_service_ind
Converting to categorical variable from numerical: ever_service_hybrid_ind
Converting to categorical variable from numerical: ever_service_lexus_sedan_ind
Converting to categorical variable from numerical: ever_service_lexus_LUV_ind
Converting to categorical variable from numerical: ever_service_camry_ind
Converting to categorical variable from numerical: ever_service_prius_ind
Converting to categorical variable from numerical: customer_high_payment_ind
Converting to categorical variable from numerical: warranty_high_payment_ind
Converting to categorical variable from numerical: high_service_ind
Converting to categorical variable from numerical: high_ro_time_ind
Converting to categorical variable from numerical: current_vehicle_serviced_ind
Converting to categorical variable from numerical: current_vehicle_customer_full_payment_ind
Converting to categorical variable from numerical: current_vehicle_high_service_ind
Converting to categorical variable from numerical: current_vehicle_high_ro_time_ind
Converting to categorical variable from numerical: has_nsh
Converting to categorical variable from numerical: pma_lexus_dealership_region
Converting to categorical variable from numerical: customer_language_english_ind
Converting to categorical variable from numerical: customer_language_spanish_ind
Total number of numerical Variables changed to Categorical Variable is 284

 Time taken to execute the function is 0.34853076934814453

X_encoded= data_prep.label_encode(X_out)

Number of categorical features: 57

 Time taken to execute the function is 0.1913771629333496

X_encoded

X_encoded.shape

(1000, 430)

Feature selection¶

embeded_lr_feature, X_new=feature_selection(X_encoded, y, 'rf')

40 selected features

/root/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:248: FutureWarning:

The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.

X_new.shape

(1000, 40)

Data Balancing¶

y.value_counts()

0    996
1      4
Name: dep_purchase_lexus_new_sedan_6mo, dtype: int64

65-35¶

train= data_prep.data_balancing(pd.concat([X_encoded, y], axis=1),y.name)

Time taken to excecute this function is  0.011549949645996094

train[y.name].value_counts()

0    7
1    4
Name: dep_purchase_lexus_new_sedan_6mo, dtype: int64

50-50¶

rus, id_rus, X_encoded_rus, y_rus= data_prep.random_undersample(X_encoded, y, label= "Random UnderSampling", plot=False)

Removed indexes: [667 203 715 178   1 530 579 848]

y_rus.value_counts()

1    4
0    4
Name: dep_purchase_lexus_new_sedan_6mo, dtype: int64

X_train, X_test, y_train, y_test= data_split.train_test_split(X_encoded, y)

Ml models¶

log=mlModels.logistic_regression(X_train[embeded_lr_feature], y_train, X_test[embeded_lr_feature], y_test, max_iter=300, random_state=1)

/root/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:1296: UserWarning:

'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 72.

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

/root/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:626: Warning:

The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

cross_val_score [0.51655629 0.98013245 0.51333333 0.37583893 0.16107383]
accuracy 89.2
balanced_accuracy_score 44.95967741935484
f1_score 47.14587737843552
precision_score 98.31822222222223
Log loss 3.730267810393374
recall_score 0.0

/root/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

/root/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py:297: RuntimeWarning:

overflow encountered in exp

roc_auc_score 44.95967741935484
confusion_matrix [[223  25]
 [  2   0]]
classification_report              precision    recall  f1-score   support

           0       0.99      0.90      0.94       248
           1       0.00      0.00      0.00         2

   micro avg       0.89      0.89      0.89       250
   macro avg       0.50      0.45      0.47       250
weighted avg       0.98      0.89      0.94       250


 Time taken to execute the Logistic_Regression is 1.2447199821472168

Dated on2019-02-20 05:03:44.944336

rf=mlModels.rf(X_train[embeded_lr_feature], y_train, X_test[embeded_lr_feature], y_test,n_estimators=250)

/root/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:626: Warning:

The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.

cross_val_score [0.99337748 0.99337748 1.         1.         1.        ]
accuracy 99.2
balanced_accuracy_score 50.0
f1_score 49.79919678714859
precision_score 98.40639999999999
Log loss 0.27631021115928645
recall_score 0.0

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

F-score is ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples.

roc_auc_score 57.45967741935484
confusion_matrix [[248   0]
 [  2   0]]
classification_report              precision    recall  f1-score   support

           0       0.99      1.00      1.00       248
           1       0.00      0.00      0.00         2

   micro avg       0.99      0.99      0.99       250
   macro avg       0.50      0.50      0.50       250
weighted avg       0.98      0.99      0.99       250


 Time taken to execute the rf is 0.4682657718658447

Dated on2019-02-20 05:04:42.495487

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

xgb=mlModels.xgb(X_train[embeded_lr_feature], y_train, X_test[embeded_lr_feature], y_test,n_estimators=250, max_depth=4)

/root/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:626: Warning:

The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.

cross_val_score [0.99337748 0.99337748 1.         1.         1.        ]
accuracy 99.2
balanced_accuracy_score 50.0
f1_score 49.79919678714859
precision_score 98.40639999999999
Log loss 0.27631021115928645
recall_score 0.0

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

F-score is ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples.

roc_auc_score 39.11290322580645
confusion_matrix [[248   0]
 [  2   0]]
classification_report              precision    recall  f1-score   support

           0       0.99      1.00      1.00       248
           1       0.00      0.00      0.00         2

   micro avg       0.99      0.99      0.99       250
   macro avg       0.50      0.50      0.50       250
weighted avg       0.98      0.99      0.99       250


 Time taken to execute the XGB is 0.15424013137817383

Dated on2019-02-20 05:06:16.645444

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.

xgb

model_evaluation.model_evaluation_test(X_test[embeded_lr_feature], y_test, xgb, alg_name='XGB Model', feat_imp=True, roc=True)

Provided model : 
 XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
       max_depth=4, min_child_weight=1, missing=0, n_estimators=250,
       n_jobs=-1, nthread=None, objective='binary:logistic',
       random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
       seed=None, silent=True, subsample=1)
accuracy  99.2
f1_score  49.79919678714859
precision_score  98.40639999999999
recall_score  0.0
confusion_matrix 
 [[248   0]
 [  2   0]]
association_type 0.010526316
state_residence 0.021052632
hh_income 0.11578947
home_ownership 0.0
interest_in_fitness_cd 0.0
midrange_car_vs_trucks_scr 0.021052632
mortage_amt_ranges_cd 0.23157895
interest_in_sports_prop_cd 0.0
mail_adr_latitude 0.21052632
customer_id 0.031578947
owned_lexus_new_sedan_ind 0.0
owned_lexus_cpo_sedan_ind 0.0
past_owned_is_ind 0.0
purchased_vehicle_big_engine_ind 0.0
last_purchase_vehicle_msrp 0.0
first_purchase_age 0.0
last_purchase_age 0.010526316
days_since_first_purchase_vehicle 0.0
age_dt 0.010526316
age_no 0.0
i1_education 0.0
communication_received_ind 0.0
campaign_clicked_ind 0.0
active_offer_ind 0.0
avg_warrpay_recent 0.021052632
avg_warrpay 0.031578947
no_days_since_firstro 0.021052632
no_ro_visits_24_36mo 0.0
avg_cust_pay_24_36mo 0.0
no_ro_visits_18_24mo 0.0
max_cust_pay_12_18mo 0.010526316
avg_ro_time_12_18mo 0.0
ratio_avg_cust_pay 0.06315789
no_repairs_after_warranty_most_recent_veh 0.0
avg_no_repairs_after_warranty 0.0
total_part_amt 0.0
avg_service_assoc_length 0.17894737
max_service_assoc_length 0.0
warranty_high_payment_ind 0.0
household_id 0.010526316
roc_auc_score 39.11290322580645

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

F-score is ill-defined and being set to 0.0 in labels with no predicted samples.

/root/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples.

	customer_id	customer_type	no_owned_vehicle	no_new_purchase_vehicle	no_used_purchase_vehicle	no_cpo_purchase_vehicle	avg_assoc_length	max_assoc_length	no_sales_dealer_assoc	days_since_last_purchase_vehicle	...	dep_total_pay_lexus_48mo	dep_customer_pay_lexus_48mo	dep_warranty_pay_lexus_48mo	dep_total_pay_lexus_60mo	dep_customer_pay_lexus_60mo	dep_warranty_pay_lexus_60mo
22902	127893539	PERSON	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.00	0.00	0.00	0.00	0.00	0.00
6606	26068086	PERSON	6.0	6.0	NaN	NaN	551.833333	1098.0	2.0	526.0	...	472.50	0.00	0.00	472.50	0.00	0.00
4329	17114303	PERSON	1.0	NaN	NaN	NaN	5777.000000	5777.0	NaN	NaN	...	0.00	0.00	0.00	0.00	0.00	0.00
5936	23320874	PERSON	2.0	2.0	NaN	NaN	1570.000000	2305.0	1.0	835.0	...	2423.53	2157.28	266.25	2423.53	2157.28	266.25
4306	17357699	PERSON	1.0	NaN	NaN	NaN	1416.000000	1416.0	NaN	NaN	...	0.00	0.00	0.00	0.00	0.00	0.00

	Col Nonmiss	col miss	col mean	col min	col max	col std	col median	0.5
customer_id	1000	0	5.325981e+07	17469.000000	1.459829e+08	3.883656e+07	4.102639e+07	4.102639e+07
no_owned_vehicle	786	214	1.540712e+00	1.000000	7.000000e+00	1.036406e+00	1.000000e+00	1.000000e+00
no_new_purchase_vehicle	380	620	1.352632e+00	1.000000	7.000000e+00	8.357882e-01	1.000000e+00	1.000000e+00
no_used_purchase_vehicle	56	944	1.000000e+00	1.000000	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00
no_cpo_purchase_vehicle	92	908	1.043478e+00	1.000000	2.000000e+00	2.050486e-01	1.000000e+00	1.000000e+00
avg_assoc_length	786	214	2.014023e+03	2.000000	9.738000e+03	1.783864e+03	1.469500e+03	1.469500e+03
max_assoc_length	786	214	2.305669e+03	2.000000	9.738000e+03	1.929669e+03	1.713000e+03	1.713000e+03
no_sales_dealer_assoc	502	498	1.071713e+00	0.000000	3.000000e+00	2.875262e-01	1.000000e+00	1.000000e+00
days_since_last_purchase_vehicle	502	498	1.652478e+03	2.000000	3.648000e+03	1.132913e+03	1.556000e+03	1.556000e+03
days_since_last_sold_vehicle	236	764	1.282089e+03	4.000000	3.458000e+03	1.021542e+03	1.064500e+03	1.064500e+03
avg_zip3_vehicle_val	995	5	4.154465e+04	15656.490066	4.658810e+04	1.884681e+03	4.156662e+04	4.156662e+04
owned_lexus_sedan_ind	1000	0	4.610000e-01	0.000000	1.000000e+00	4.987261e-01	0.000000e+00	0.000000e+00
owned_lexus_LUV_ind	1000	0	3.650000e-01	0.000000	1.000000e+00	4.816711e-01	0.000000e+00	0.000000e+00
owned_lexus_new_luv_ind	1000	0	2.590000e-01	0.000000	1.000000e+00	4.383048e-01	0.000000e+00	0.000000e+00
owned_lexus_cpo_luv_ind	1000	0	5.300000e-02	0.000000	1.000000e+00	2.241456e-01	0.000000e+00	0.000000e+00
owned_lexus_new_sedan_ind	1000	0	3.220000e-01	0.000000	1.000000e+00	4.674768e-01	0.000000e+00	0.000000e+00
owned_lexus_cpo_sedan_ind	1000	0	8.000000e-02	0.000000	1.000000e+00	2.714289e-01	0.000000e+00	0.000000e+00
owned_toyota_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_toyota_scion_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_lexus_ind	1000	0	7.720000e-01	0.000000	1.000000e+00	4.197525e-01	1.000000e+00	1.000000e+00
owned_scion_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_camry_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_corolla_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_rav4_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_prius_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_truck_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_suv_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
owned_hybrid_ind	1000	0	7.500000e-02	0.000000	1.000000e+00	2.635231e-01	0.000000e+00	0.000000e+00
owned_big_engine_vehicle_ind	1000	0	7.300000e-02	0.000000	1.000000e+00	2.602667e-01	0.000000e+00	0.000000e+00
owned_4wd_vehicle_ind	1000	0	2.960000e-01	0.000000	1.000000e+00	4.567194e-01	0.000000e+00	0.000000e+00
...	...	...	...	...	...	...	...	...
no_ro_visits_post_2_4_tc	668	332	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
no_ro_visits_post_4_6_tc	668	332	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
no_ro_visits_post_over6_tc	668	332	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
last_serviced_dealer_no	668	332	6.047934e+04	2021.000000	6.483000e+04	8.586923e+03	6.120700e+04	6.120700e+04
last_serviced_dealer_region	668	332	3.248054e+01	11.000000	8.000000e+01	4.333328e+00	3.300000e+01	3.300000e+01
ro_after_ocpe_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
ever_customer_payment_ind	1000	0	5.550000e-01	0.000000	1.000000e+00	4.972145e-01	1.000000e+00	1.000000e+00
ever_warranty_payment_ind	1000	0	5.340000e-01	0.000000	1.000000e+00	4.990923e-01	1.000000e+00	1.000000e+00
ever_service_ind	1000	0	6.680000e-01	0.000000	1.000000e+00	4.711666e-01	1.000000e+00	1.000000e+00
ever_service_hybrid_ind	1000	0	6.700000e-02	0.000000	1.000000e+00	2.501471e-01	0.000000e+00	0.000000e+00
ever_service_lexus_sedan_ind	1000	0	3.890000e-01	0.000000	1.000000e+00	4.877673e-01	0.000000e+00	0.000000e+00
ever_service_lexus_LUV_ind	1000	0	3.300000e-01	0.000000	1.000000e+00	4.704480e-01	0.000000e+00	0.000000e+00
ever_service_camry_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
ever_service_prius_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
customer_high_payment_ind	1000	0	2.240000e-01	0.000000	1.000000e+00	4.171307e-01	0.000000e+00	0.000000e+00
warranty_high_payment_ind	1000	0	1.320000e-01	0.000000	1.000000e+00	3.386601e-01	0.000000e+00	0.000000e+00
high_service_ind	1000	0	3.380000e-01	0.000000	1.000000e+00	4.732652e-01	0.000000e+00	0.000000e+00
high_ro_time_ind	1000	0	3.730000e-01	0.000000	1.000000e+00	4.838441e-01	0.000000e+00	0.000000e+00
current_vehicle_serviced_ind	1000	0	6.680000e-01	0.000000	1.000000e+00	4.711666e-01	1.000000e+00	1.000000e+00
current_vehicle_customer_full_payment_ind	1000	0	5.620000e-01	0.000000	1.000000e+00	4.963894e-01	1.000000e+00	1.000000e+00
current_vehicle_high_service_ind	1000	0	3.380000e-01	0.000000	1.000000e+00	4.732652e-01	0.000000e+00	0.000000e+00
current_vehicle_high_ro_time_ind	1000	0	3.730000e-01	0.000000	1.000000e+00	4.838441e-01	0.000000e+00	0.000000e+00
has_nsh	1000	0	6.680000e-01	0.000000	1.000000e+00	4.711666e-01	1.000000e+00	1.000000e+00
household_id	1000	0	3.355509e+08	69458.000000	6.488677e+08	2.450549e+08	3.459935e+08	3.459935e+08
pma_toy_scion_dealership_cd	988	12	2.049720e+04	0.000000	9.999900e+04	1.545607e+04	1.605400e+04	1.605400e+04
pma_toy_scion_dealership_region	1000	0	3.230400e+01	0.000000	8.000000e+01	2.322423e+01	2.100000e+01	2.100000e+01
pma_lexus_dealership_cd	988	12	5.544554e+04	0.000000	9.999900e+04	1.932264e+04	6.120300e+04	6.120300e+04
pma_lexus_dealership_region	1000	0	2.872700e+01	0.000000	3.400000e+01	1.062569e+01	3.300000e+01	3.300000e+01
customer_language_english_ind	1000	0	3.400000e-01	0.000000	1.000000e+00	4.739458e-01	0.000000e+00	0.000000e+00
customer_language_spanish_ind	1000	0	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00

	Percentage of zeros
days_since_last_communication_toyota	215.517241
no_distinct_communication_dates_toyota	215.517241
no_communication_received_toyota	215.517241
no_communication_dates_toyota	215.517241
days_since_first_communication_toyota	215.517241
no_communication_opened_toyota	215.517241
no_campaign_opened_toyota	215.517241
no_communication_clicked_toyota	215.517241
no_campaign_clicked_toyota	215.517241
no_directmail_campaign_received	215.517241
no_web_campaign_received	215.517241
no_email_campaign_received_toyota	215.517241
no_directmail_campaign_received_toyota	215.517241
no_directmail_campaign_received_lexus	215.517241
no_web_campaign_received_lexus	215.517241
no_web_campaign_received_toyota	215.517241
no_ocpe_email	215.517241
days_lapsed_toyota_care_expiry	215.517241
days_remain_toyota_care_expiry	215.517241
no_tc_completed	215.517241
no_ocpe	215.517241
no_ocpe_direct	215.517241
no_ocpe_phone	215.517241
seeking_cred	215.301724
auto_in_the_market_cd	215.301724
no_brochures_requested	214.439655
days_since_last_brochure_request	214.439655
days_since_first_lead	203.448276
days_since_last_lead	203.448276
no_handraiser	203.448276
...	...
owned_toyota_van_ind	0.000000
past_owned_toyota_compact_ind	0.000000
past_owned_toyota_compactsuv_ind	0.000000
past_owned_toyota_fullsizesuv_ind	0.000000
past_owned_toyota_luxurymidsize_ind	0.000000
tc_only2_ind	0.000000
past_owned_toyota_midsize_ind	0.000000
past_owned_toyota_midsizesuv_ind	0.000000
past_owned_toyota_pickup_ind	0.000000
past_owned_toyota_premiummidsize_ind	0.000000
customer_language_english_ind	0.000000
past_owned_toyota_subcompact_ind	0.000000
owned_toyota_sportscar_ind	0.000000
owned_toyota_premiummidsize_ind	0.000000
owned_toyota_pickup_ind	0.000000
owned_toyota_midsizesuv_ind	0.000000
owned_toyota_midsize_ind	0.000000
owned_toyota_luxurymidsize_ind	0.000000
owned_toyota_hybrid_ind	0.000000
owned_toyota_fullsizesuv_ind	0.000000
owned_toyota_compactsuv_ind	0.000000
owned_toyota_compact_ind	0.000000
owned_toyota_sport_ind	0.000000
owned_toyota_family_ind	0.000000
owned_toyota_sedan_ind	0.000000
first_purchase_luxury_vehicle_ind	0.000000
tc_only5_ind	0.000000
tc_only4_ind	0.000000
tc_only3_ind	0.000000
customer_id	0.000000

	Percentage of ones
owner_ind	100.0
owned_lexus_ind	77.2
has_nsh	66.8
current_vehicle_serviced_ind	66.8
ever_service_ind	66.8
dealer_loyalist	62.4
owned_luxury_no	57.6
purchased_vehicle_lexus_ind	56.4
current_vehicle_customer_full_payment_ind	56.2
ever_customer_payment_ind	55.5
no_owned_vehicle	54.6
ever_warranty_payment_ind	53.4
current_owned_lexus_ind	51.0
no_ro_vehicle	50.4
owned_vehicle_my_2001_10_ind	49.2
owned_other_color_vehicle_ind	48.6
first_purchase_luxury_vehicle_ind	47.2
first_purchase_ind	47.2
first_purchase_lexus_ind	47.2
dofu_year2_ind	46.9
no_sales_dealer_assoc	46.7
owned_lexus_sedan_ind	46.1
has_emd	44.5
campaign_received_ind	43.3
communication_received_ind	43.3
offer_received_ind	42.7
no_servicing_dealer	41.8
active_offer_ind	41.6
purchased_vehicle_new_ind	39.5
ever_service_lexus_sedan_ind	38.9
...	...
no_ocpe_phone	0.0
no_ocpe_direct	0.0
no_ocpe_email	0.0
past_owned_toyota_hybrid_ind	0.0
past_owned_toyota_luxurymidsize_ind	0.0
past_owned_toyota_midsize_ind	0.0
current_owned_toyota_midsize_ind	0.0
past_owned_toyota_sport_ind	0.0
past_owned_toyota_sedan_ind	0.0
past_owned_toyota_family_ind	0.0
current_owned_toyota_van_ind	0.0
current_owned_toyota_subcompactsuv_ind	0.0
current_owned_toyota_subcompact_ind	0.0
current_owned_toyota_sportscar_ind	0.0
current_owned_toyota_premiummidsize_ind	0.0
current_owned_toyota_pickup_ind	0.0
current_owned_toyota_midsizesuv_ind	0.0
current_owned_toyota_luxurymidsize_ind	0.0
past_owned_toyota_midsizesuv_ind	0.0
current_owned_toyota_hybrid_ind	0.0
current_owned_toyota_fullsizesuv_ind	0.0
current_owned_toyota_compactsuv_ind	0.0
current_owned_toyota_compact_ind	0.0
past_owned_toyota_van_ind	0.0
past_owned_toyota_subcompactsuv_ind	0.0
past_owned_toyota_subcompact_ind	0.0
past_owned_toyota_sportscar_ind	0.0
past_owned_toyota_premiummidsize_ind	0.0
past_owned_toyota_pickup_ind	0.0
customer_language_spanish_ind	0.0

	Mode
customer_id	17469
customer_type	PERSON
no_owned_vehicle	1
no_new_purchase_vehicle	1
no_used_purchase_vehicle	1
no_cpo_purchase_vehicle	1
avg_assoc_length	24
max_assoc_length	24
no_sales_dealer_assoc	1
days_since_last_purchase_vehicle	62
days_since_last_sold_vehicle	54
brand_relation	Lexus
active_brand	Lexus
association_type	Active
state_residence	CA
avg_zip3_vehicle_val	42617.5
owned_lexus_sedan_ind	0
owned_lexus_LUV_ind	0
owned_lexus_new_luv_ind	0
owned_lexus_cpo_luv_ind	0
owned_lexus_new_sedan_ind	0
owned_lexus_cpo_sedan_ind	0
owned_toyota_ind	0
owned_toyota_scion_ind	0
owned_lexus_ind	1
owned_scion_ind	0
owned_camry_ind	0
owned_corolla_ind	0
owned_rav4_ind	0
owned_prius_ind	0
...	...
ever_customer_payment_ind	1
ever_warranty_payment_ind	1
ever_service_ind	1
ever_service_hybrid_ind	0
ever_service_lexus_sedan_ind	0
ever_service_lexus_LUV_ind	0
ever_service_camry_ind	0
ever_service_prius_ind	0
customer_high_payment_ind	0
warranty_high_payment_ind	0
high_service_ind	0
high_ro_time_ind	0
current_vehicle_serviced_ind	1
current_vehicle_customer_full_payment_ind	1
current_vehicle_high_service_ind	0
current_vehicle_high_ro_time_ind	0
has_nsh	1
household_id	69458
customer_zipcode	00000
pma_toy_scion_dealership_cd	0
pma_toy_scion_dealership_region	50
pma_toy_scion_dealership_markettype	Metro Market
pma_lexus_dealership_cd	0
pma_lexus_dealership_region	34
pma_lexus_dealership_markettype	Metro Point
mail_adr_latitude	+17971368
mail_adr_longitude	-066014586
deceased_ind	N
customer_language_english_ind	0
customer_language_spanish_ind	0